Electricity supply and pricing in a utility scale “smart grid” electrical network offers significant improvements in energy security, reliability, efficiency and lower cost to eventual end-users. In these systems, electrical generators using a variety of fuel types and having specific attributes related to the generator type compete to supply time dependent power blocks to the smart grid and offer reliable on-demand scheduling with guaranteed cost to supply. Generators participating in the smart grid receive higher utilisation and as a result improved operating efficiency. Several large scale smart grids world-wide have evolved that involve a central scheduling authority or market operator to manage and solve temporal demand solutions across large geographic areas in order to supply a number of specific demand regions within the larger geographic area. One example of such a smart grid electrical network is the national electricity market (NEM) in Australia as managed by the Australian Energy Market Operator (AEMO).
Demand forecasting is employed in smart grid electrical networks to predict temporal demand profiles across timescales of several minutes to weeks in advance. For any given forecast pattern of demand over time, there will be an associated optimal mix of generation and required network interconnections. Using accurate cost and scheduling models for the smart grid participants, namely the generators and transmission and distribution networks, a temporal “spot price” is evaluated against the demand profile forecast. Because generation of large scale electricity cannot be economically and efficiently stored or buffered within the electrical network, and also given the tendency for consumers towards continuous consumption, the electricity generators' output is required to match demand in real-time, ie instantaneously.
While hydroelectricity can be used to store excess energy from the grid it can only recover a small portion of this stored energy. For a smart grid spanning several distinct demand regions there exists the opportunity to trade surplus generated capacity or acquire additional capacity that was not originally accounted for in a given region's forecasted demand. This is a prime advantage of the network based energy supply model of smart grid electrical networks.
As referred to previously, Australia has implemented a fully integrated national smart grid, which is operated as an energy only, gross pool market, meaning that all energy is traded through a central clearing mechanism or market operator (ie the AEMO). A market clearing price is calculated by the market operator for each half hour trading interval based on the bids and offers of generators and retailers. A separate spot price is calculated in this way for each of the geographic regions of the NEM. In order for generators to sell energy into the wholesale market, generators submit offers to the AEMO detailing the volumes which can be generated and the prices for supplying. AEMO uses these offers to determine the most cost-effective way to meet the forecasted demand. The forecasted demand over a 24-hour look ahead is calculated and updated every half hour by the AEMO. Offers to generate electricity are then stacked in merit order of rising price with this merit order then used by the market operator to dispatch generators on a least cost first schedule.
The point on the merit order at which demand is satisfied determines a single price for electricity in each region of the NEM which is the Regional Reference Price (RRP). Generators within a NEM region then receive the RRP or spot price for the volume of generation for which they are dispatched. In general, the smart grid electrical network functioning as a supply and demand system tends to track regional demand extremely well with the settled temporal spot price or RRP at a given time (which is typically updated publically every 15 minutes) closely tracking the forecast RRP. It can be seen that spot price variations will reflect forecasted peak and off-peak demand patterns. While spot prices variations tend to follow an average pattern throughout most days of the year, at certain times they may also exhibit significant volatility reflecting, for example, seasonal/temperature fluctuations, outages and network interruptions.
Referring now to FIG. 1, there is shown a schematic representation of a smart grid electrical network 100 as described above. Electrical power is generated by generators 110 and transported to the end-user 130 via transmission and distribution networks 115/120 with the flow of monies then exchanged between the generator 110 and retailer 125 via the central clearing entity being the market operator 105. The end-user 130 interacts only with the retailer 125 for cost recovery. The end-user's 130 consumption is monitored via a measuring device (MD) (eg an electricity power meter) which is used by the retailer 125 to determine the amount of electrical power used and invoice the end-user 130. In this arrangement, the actual temporal wholesale spot price can vary considerably but this variation is not visible to the end-user 130 who will be paying a fixed or semi-fixed cost over a contracted time period.
A given retailer participating in the NEM representing a group of end-users 130 must therefore develop a risk based tariff schedule to reflect the cost of supplying electricity to a captive group of end-users 130 which is acceptable over the contracted time period which may be in the order of months or even years. The final retail cost (RC) presented to the end-user 130 by the retailer 125 comprises the anticipated purchase cost of electricity directly from the market operator 105 (WC), transmission network cost (TNC), distribution network cost (DNC) from the grid to the physical region of the end-user, end-user metering costs (MRC), retail operating costs (ROPC) (including, hedging future funds, market participation costs, credit notes for market purchases, customer billing and marketing) and retailer profit margin (RPM). Other costs such as government levies (GL) and environmental schemes (ESC) and feed-in tariff costs (FITC) are also passed through to the end-user 130.
The total retail cost RC then presented to the end-user is as a result RC=WC+TNC+DNC+MRC+ROPC+RPM+FITC+ESC+GL. Approximate proportions of the components comprising the RC are: WC=25%, TNC=26%, DNC=31%, (ROPC+RPM)=12%, FITC<0.5%, GL=1.5%, ESC=3%. This data is representative of the Australian 2012/2013 electricity cost breakdown and is not expected to vary substantially from other smart grid type networks in developed markets. As is evident from the above breakdown, total network costs constitute a major share (57%) of the total cost. The only time varying costs exposed to the retailer are WC and ROPC due to direct exposure to the electricity market. Network access costs are in general fixed over a period of several years. However, future market innovation will augment transmission network costs to be reflective of the actual power transferred between nodes comprising the transmission network and would be reflected in the market operator forecast.
The capital intensive nature of the electricity generation business means that it is not feasible to solely base the revenue streams around potentially volatile spot market prices. Equally, retailers 125 may be exposed to significantly high pool prices from time to time, which they must cover in order to supply their end-users 130. Accordingly, generation and retailer participants have developed a number of mechanisms to manage their exposure to this volatility, namely the use of futures contracts and/or vertically integrating generator with retail entities to form what are termed “gentailers”.
Transmission and distribution networks further face pressure of providing increased network capacity to support the extremes of peak demand which occur for only small periods of the year. As an example, in Australia the maximum peak demand occurs for only 100 hours of the year, with 60% or more of the network capacity utilised for only 37% of the year. It follows that the network capacity utilisation (ratio of peak demand to average demand) for a majority of the time is therefore extremely poor.
To alleviate the need for increasing network capacity in order to meet the peak demand only, which as would be appreciated carries an extremely high capital cost penalty, there is a need to suppress peak demand events. As can be seen from above, networks costs (ie both DNC and TNC) are recovered by passing through a fixed cost to the end-user over a long time. Peak demand events are fundamentally constrained by the network node and transport link capacities. Therefore, large market anomalies can occur during these peak demand events generating extreme excursions in the spot price. This has been seen as a market failure of these types of smart grid market arrangements, ie the failure to reduce peak demand, leading to higher costs to the end-user 130 and also leading to power outages on those occasions where electrical power requirements cannot be met.
Yet a further market failure is the poor utilisation of renewable energy generation within the grid. As an example, wind energy is semi-dispatchable by the very nature of the wind source. Government policies generally mandate priority based use of renewable energy when available for supply to the smart grid electrical network. This is in preference even to dispatched fossil-fuel based generators scheduled to meet any demand event. For example, under these policies wind energy will displace fossil-fuel generation and as a result fossil-fuel generators then seek compensation from the market operator. Furthermore, wind generation capacity once available may exceed the required demand in their particular region and thus there is a need for this excess capacity to be used or dissipated and “lost” within the grid. These events typically do not occur during peak demand and thus the cost of electricity available in these circumstances is extremely low. In fact, a negative pricing event can occur if renewable energy cannot be shed.
In an attempt to better regulate end-user demand and as a result of improve the forecasting by the market operator, a range of flexible pricing offers have been made available by retailers such as time-of-use (TOU) tariffs as compared to block accumulation power tariffs. TOU is particularly advantageous for retailers and end-users alike in order to provide a time of day indicator for cost reflective pricing of electricity. The retail TOU pricing for end-user consumption is intended to reflect the relative levels of supply and demand at a particular time of day. In general, it is anticipated that cost reflective tariffs such as these will over the long term force end-user consumptions habits to reduce overall peak demand resulting in overall increased network utilisation and reduction in capital expenditure.
While TOU pricing is able to provide end-users with access to lower cost electricity the timing of this off-peak access is not generally compatible with the daily consumption profiles of end-users. As the TOU tariffs are set over as contracted period which is normally in the range of months to years, it can be seen that these tariff arrangements do not in the short term provide timely and accurate price signals required to stimulate a desirable response by end-users such as reducing peak demand. Furthermore, any reduced peak demand by an end-user is not at present reflected as a cost benefit signal to the electrical network for performing this action, eg by modifying tariff schedules or the like to induce or encourage further reductions of peak demand by an ends-user.
There is therefore a need to structure smart grid electrical networks to reduce peak demand events to improve network utilisation. There is also a need for a more effective and timely exchange of information between end-users and the smart grid electrical network to facilitate more efficient use of the network as a whole.